Video description
Data science is quickly becoming one of the most promising careers in the twentyfirst century. It is automated, programdriven, and analytical. As a result, it’s no surprise that the demand for data scientists has been expanding in the job market over the last few years.
We will begin with a quick refresher on Python fundamentals for beginners in this course. This is optional; if you’re already familiar with Python, skip to the next chapter.
Data science will be the topic of the next three sections. We will start with the essential Python libraries for data science, then go on to the fundamental NumPy properties, and lastly begin with mathematics and how to use it in data science.
You will learn about Python Pandas DataFrames and series after learning about data science. Following that, we will get down to business and begin data cleaning. Following that, we will learn how to use Python to visualize data and do data analysis on some sample datasets. Finally, we will cover the Time series in Python and learn how to work with and convert datasets to Time series.
By the end of this course, you will be able to execute data manipulation for data science in Python with ease.
What You Will Learn
 A quick refresher to Python fundamentals
 Learn to use Pandas for data analysis
 Learn to work with numerical data in Python
 Learn statistics and math with Python
 Learn how to code in Jupyter Notebook
 Learn how to install packages in Python
Audience
This course is open to students of all skill levels, and you will be able to succeed even if you have no prior programming or statistical knowledge.
About The Author
Meta Brains: Meta Brains is a team of passionate software developers and finance professionals. They provide professional training programs that combine their expertise in coding, finance, and Excel.
With a focus on the Metaverse, they aim to equip learners with the necessary skills to participate in the next computing revolution. Their inclusive approach ensures accessibility to everyone, fostering a community that collaboratively codes and builds the future of the Metaverse.
Table of contents

Chapter 1 : Python Quick Refresher (Optional)
 Welcome to the course!
 Introduction to Python
 Setting up Python
 What is Jupyter?
 Anaconda Installation: Windows, Mac, and Ubuntu
 How to Implement Python in Jupyter?
 Managing Directories in Jupyter Notebook
 Input/Output
 Working with Different Datatypes
 Variables
 Arithmetic Operators
 Comparison Operators
 Logical Operators
 Conditional Statements
 Loops
 Sequences: Lists
 Sequences: Dictionaries
 Sequences: Tuples
 Functions: Builtin Functions
 Functions: UserDefined Functions
 Chapter 2 : Essential Python Libraries for Data Science
 Chapter 3 : Fundamental NumPy Properties

Chapter 4 : Mathematics for Data Science
 Basic NumPy Arrays: zeros()
 Basic NumPy Arrays: ones()
 Basic NumPy Arrays: full()
 Adding a Scalar
 Subtracting a Scalar
 Multiplying by a Scalar
 Dividing by a Scalar
 Raise to a Power
 Transpose
 ElementWise Addition
 ElementWise Subtraction
 ElementWise Multiplication
 ElementWise Division
 Matrix Multiplication
 Statistics

Chapter 5 : Python Pandas DataFrames and Series
 What is a Python Pandas DataFrame?
 What is a Python Pandas Series?
 DataFrame versus Series
 Creating a DataFrame Using Lists
 Creating a DataFrame Using a Dictionary
 Loading CSV Data into Python
 Changing the Index Column
 Inplace
 Examining the DataFrame: Head and Tail
 Statistical Summary of the DataFrame
 Slicing Rows Using Bracket Operators
 Indexing Columns Using Bracket Operators
 Boolean List
 Filtering Rows
 Filtering rows using ‘’ and ‘’ Operators
 Filtering Data Using loc()
 Filtering Data Using iloc()
 Adding and Deleting Rows and Columns
 Sorting Values
 Exporting and Saving Pandas DataFrames
 Concatenating DataFrames
 Groupby()
 Chapter 6 : Data Cleaning
 Chapter 7 : Data Visualization using Python

Chapter 8 : Exploratory Data Analysis
 Introduction
 What is Exploratory Data Analysis?
 Univariate Analysis
 Univariate Analysis: Continuous Data
 Univariate Analysis: Categorical Data
 Bivariate Analysis: Continuous and Continuous
 Bivariate Analysis: Categorical and Categorical
 Bivariate Analysis: Continuous and Categorical
 Detecting Outliers
 Categorical Variable Transformation
 Chapter 9 : Time Series in Python
Product information
 Title: Data Manipulation in Python  Master Python, NumPy, and Pandas
 Author(s):
 Release date: May 2022
 Publisher(s): Packt Publishing
 ISBN: 9781804614396
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